Reasoning systems (also called “expert systems”) are typically designed for a specific domain. General knowledge about the domain is captured in an ontology. Ontologies are formal models of the world of interest. For example, if the reasoning system is expected to reason about mothers or motherhood, the ontology will contain entries for just what it means (in the world of interest) to be a mother.
Note, in the art, the term “ontology” has been used in a relaxed way to apply to any reference, including ordered lists, data dictionaries and free form glossaries. The present use of the concept is the more formal, strict and powerful definition. A good reference for this definition is “Ontology: Towards a New Synthesis.” By Chris Welty and Barry Smith in “Formal Ontology in Information Systems.” edited by the same authors. ACM Press. 2001 ISBN 978-1581133776.
Ontologies, in the sense used here, are built using special types of logic called description logics. They generally require expertise and tools distinct from those of the reasoning system. Typically they are finalized before a user begins using the reasoning system that references them. Ontologies are then treated as if they are static; in this paradigm, once a domain is definitionally described, it is done.
An analogy is often made to the scientific method where laws of science are discovered and proven and thenceforth are used in reasoning over the world. They can be extended by theorists, but what is previously established rarely changes. However, a well known problem in logic is that the meaning of a fact or inference can change depending on the context in which it exists, different contexts existing in most worlds of interest. The method and system thus requires novel mechanisms to achieve this.
In domains of interest, including the real world, contexts can change while the reasoning process is underway, or between when the reasoning was performed and the results used. For example, the concept of ‘mother’ would mean a female parent in many contexts but be an epithet in another. Also, in some contexts the reasoner is expected to manage multiple meanings, perhaps as a joke or even as a way to enhance communication. Even the usual definition can vary; ‘Mother’ in one context can be a property defined by law, and in another someone who has purchasing influence.
So the problem exists that in much human reasoning, ontologies are dynamic and change basic meaning and internal structure depending on the context that applies as contexts shift. Multiple and changing ontologies are required but the current practice supports only single, static ontologies.
Another problem is that some reasoning systems have as a goal the creation of an explanatory narrative wherein they need to create or enlarge worlds, resulting in elements that have ontological weight. These might be referenced by later users of the reasoning system or any related communications such as story-telling.
Another problem is that many reasoning systems need to reason over information from different sources and which have been structured using different ontologies, methods or description logics. For example, military intelligence systems often have need to consider information collected by different means, from different media and stored in different information pools using different ontologies.
Another problem deals with reasoning over cause. Reasoning systems create new facts from existing facts and inferences, but these rarely create new facts that capture causal connections. For example, if a system reasons that ‘Aristotle is a man’ from ‘all citizens are men’ and ‘Aristotle is a citizen,’ it does not include information about what caused any of these facts to be true. Yet the purpose of many reasoning systems is to understand what causes what and possibly how to change results by employing different or changed causal dynamics. Facts and inferences change based on context, and changes in causal dynamics can also be invoked by a change of context, or contextual influence. No method (and system) currently exists, such that the interaction between multiple contexts can derive causal agents, including some elements that do not appear in any of their source ontologies.
Another problem is that both levels of the system (reasoning and ontological derivation systems) may need to operate in a distributed, federated manner, using distributed systems, involving collaborating but geographically diverse users and agents and using pools of information from many distributed sources.
The current art supports none of these needs.
The current state of reasoning systems that do recognize context are those that use non-monotonic logic, or some technique that can be mapped to such. This is effective when the change in context is not ontological, does not generate new reusable elements, does not reason over cause and prior inferences are not modified. Even in the limited cases where it does apply, the logical overhead of such systems confines them to simple or academic settings.
The current state of fieldable ontological systems is dominated by work on the Semantic Web and this work reinforces these limits.
Therefore, there exists a need for a system and method to dynamically derive ontologies or the presentation of ontological information based on changing contexts and relationships among multiple contexts. Derived ontologies will present changed or new semantics. Such a derived ontology system will retroactively modify prior inferences that used now-modified reference semantics and do so in a formerly ordered manner. In so doing, the ontological derivation system will perform higher level ‘reasoning’ over the contexts, situations and attitudes that bear on ontological shifts.
Additionally, there is a related need to reveal causal dynamics in the combined system. The ‘combined system’ is the system of dynamic ontological derivation by higher level reasoning together with the inference-driven reasoning system that dynamically readjusts. Each of these two components will have its own logic with the logics formally related. Moreover the need exists for ontological federation, and in particular federation over existing ontological methods including those of the Semantic Web.
FIG. 1 shows the current state of ontology creation systems 101 and reasoning systems 103. An ontology 102 is a created set of connected formal definitions of a world, created using a description logic. An example of a description logic as employed in the Semantic Web is SHOIN(D).
The expertise and tools required are therefore highly specialized and the common practice is that the ontology creation system 101, the logic employed therein and the supporting tool suites and methods are separate from the user of the ontology. The ontology is created before use and created elements do not change when directly being used. The ontology creation system 101 presents to a client and interface 108 to allow the user to visualize and engineer the ontology.
The user of the ontology 102 is a reasoning system 103 (historically called an ‘expert system’). Reasoning systems are varied in details, but all follow the form of accepting facts, inferences and sometime axioms. They then apply some form of logic or probabilistic association to produce inferences 105. A reasoning system 103 can combine automated and human processes. It can employ any algorithmic method that requires a semantic reference. The reasoning system presents to a client and interface system 107 that is distinct from the ontology client 108.
The resulting inferences and facts 105, when compared to an ontology 102, are structured differently. Some combined systems have the feature of ‘learning.’ In this case, a feedback system 106 exists which advises the ontology creation system. Information reported via this feedback may be an inference that is general enough to be entered into the ontology for reuse. Alternatively, it can be information that tunes the ontology.
A common example of such tuning is via constraints. An example is that the ontology may contain information that a car is capable of independent movement. A refining constraint may be that the movement is only possible if the amount of fuel is greater than zero.
Another common example is via exception. An example is that the ontology may contain information that living dogs have four legs. An exception may be that a specific living creature is a dog but possess only three legs.
In this way, the existing art can be said to support some dynamism in ontologies, but the dynamism is limited to interpretation in a single context. In these systems there can be no interpretation that is derived from the interaction between more than one ontology.
Because the current art produces static, single-context ontologies, the current art of ontology visualization and modeling is primitive, using simple graphs or their intented linearization. For example, the most widely used tool at present is Protégé, an open source project from Stanford. Users edit graphs manually. The required skill is so rarified that it is unlikely that anyone could be a competent user of the tool and a skilled ontology engineer as well as an expert in the domain being addressed.
A critical survey of these tools is Ontology Visualization Methods—A Survey, by Katifori, A., Halatsis, C., Lepouras, G., Vassilakis, C., Giannopoulou, E. ACM Computing Surveys, 39, 4, Article 10 (2007).